Integrating Terrestrial Laser Scanning, Generative AI, and Mixed Reality for Digital Heritage Reconstruction

Summary

This replicable protocol demonstrates a workflow for digital heritage reconstruction. It integrates Terrestrial Laser Scanning to capture site geometry, generative AI to reconstruct lost cultural elements from archival sources, and Mixed Reality to visualize these synthesized elements in situ.

Abstract

The rapid acceleration of urbanization in developing regions has precipitated a dual crisis for architectural heritage: the physical degradation of structures and the intangible dissipation of cultural memory. Traditional conservation methodologies, which rely predominantly on static documentation, such as photography and manual surveying, are increasingly insufficient for capturing the complex spatial and temporal dimensions of these historic sites. This study introduces a novel, combinatorial methodological framework designed to bridge the gap between rigorous archiving and dynamic public engagement. We detail a workflow that synergizes three distinct technologies: Terrestrial Laser Scanning (TLS) to capture the tangible geometric attributes of the building with millimeter-level accuracy; Conditional Generative Artificial Intelligence (CGAI) to generate historically informed visual representations of lost cultural elements (specifically, traditional temple fair structures) based on archival references; and Augmented Reality (AR) via holographic headsets to superimpose these layers into a unified Mixed Reality (MR) experience. The protocol describes the complete pipeline from data acquisition strategies and point cloud processing to AI-driven model generation and final deployment. This approach yields a dynamic digital simulacrum that successfully overlays high-precision spatial data with interpretive cultural visualizations, offering a technically feasible and scalable workflow for the digital preservation and augmented exhibition of urban heritage landmarks.

Introduction

The preservation of architectural heritage constitutes a critical imperative within the contemporary discourse of urban planning and cultural sustainability^1,2. As cities undergo rapid modernization and digitization, the tension between development and conservation becomes increasingly acute³. This phenomenon is particularly evident in the Central Plains of China, a region that serves as a cradle of civilization yet faces the relentless pressure of urban expansion^4,5. The protection of cultural heritage extends beyond the mere m....

Protocol

1. Historical data collection and site analysis

1. Initiate the project by conducting a comprehensive archival review. Retrieve historical records, photographs, architectural drawings, and textual descriptions related to the target heritage site (in this case, the City God Temple of Zhengzhou) from local archives, temple management offices, and relevant cultural heritage bureaus.
2. Analyze the textual data specifically to identify descriptions of lost ephemeral elements, such as temple fair decorations, temporary stalls, banners, and specific lighting arrangements (e.g., “red lanterns,” “dragon dances”).
3. Conduct a preliminar....

Results

The terrestrial laser scanning campaign, executed in November 2024, utilized 18 scan stations to achieve comprehensive coverage of the Main Hall, Entrance Plaza, and Apse zones. As detailed in Table 1, the scanning parameters were strictly calibrated—specifically utilizing a resolution setting of 1/4 and a quality setting of 4x—to yield a point distance of approximately 6.1 mm at a 10-meter range. This specific calibration was essential to balance high data density with on-site acquisition efficiency. Co.......

Discussion

This study successfully demonstrates a combinatorial methodological framework for the digital renaissance of architectural heritage. By integrating Terrestrial Laser Scanning (TLS), Conditional Generative AI (CGAI), and Augmented Reality (AR), we have established a workflow that transcends the limitations of traditional static preservation, as summarized in the methodological framework (Figure 8).

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
DSLR camera	High-resolution reference photography	N/A
FARO Focus3D S120	Terrestrial Laser Scanner (TLS)	FARO Technologies
FARO SCENE 2019	Point cloud data processing software	FARO Technologies
Fologram	MR development plugin and headset application	Fologram
High-performance workstation	Data processing and rendering	N/A
Microsoft HoloLens 2	Mixed Reality (MR) headset	Microsoft
Rhino 8	3D modeling environment	Robert McNeel & Associates
SD storage card	Raw scan data storage	N/A
Stable Diffusion	Conditional Generative AI (CGAI) model	Stability AI
Tencent Huiyuan 3D	Conditional Generative AI (CGAI) platform	Tencent